title: Estimating the transmission dynamics of Streptococcus pneumoniae from strain prevalence data.
abstract: Streptococcus pneumoniae is a typical commensal bacterium causing severe diseases. Its prevalence is high among young children attending day care units, due to lower levels of acquired immunity and a high rate of infectious contacts between the attendees. Understanding the population dynamics of different strains of S.pneumoniae is necessary, for example, for making successful predictions of changes in the composition of the strain community under intervention policies. Here we analyze data on the strains of S. pneumoniae carried in attendees of day care units in the metropolitan area of Oslo, Norway. We introduce a variant of approximate Bayesian computation methods, which is suitable for estimating the parameters governing the transmission dynamics in a setting where small local populations of hosts are subject to epidemics of different pathogenic strains due to infections independently acquired from the community. We find evidence for strong between-strain competition, as the acquisition of other strains in the already colonized hosts is estimated to have a relative rate of 0.09 (95% credibility interval [0.06, 0.14]). We also predict the frequency and size distributions for epidemics within the day care unit, as well as other epidemiologically relevant features. The assumption of ecological neutrality between the strains is observed to be compatible with the data. Model validation checks and the consistency of our results with previous research support the validity of our conclusions.
authors.affiliation: Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, 00014, Helsinki, Finland.
authors.forename: Elina
authors.initials: E
authors.lastname: Numminen
authors.forename: Lu
authors.initials: L
authors.lastname: Cheng
authors.forename: Mats
authors.initials: M
authors.lastname: Gyllenberg
authors.forename: Jukka
authors.initials: J
authors.lastname: Corander
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D002353:Carrier State / Q000453:epidemiology / Q000382:microbiology / Q000635:transmission; D002656:Child Day Care Centers; D002675:Child, Preschool; D003198:Computer Simulation; D058872:Epidemics / Q000706:statistics & numerical data; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009664:Norway / Q000453:epidemiology; D011008:Pneumococcal Infections / Q000453:epidemiology / Q000382:microbiology* / Q000635:transmission*; D015995:Prevalence; D013045:Species Specificity; D013269:Stochastic Processes; D013296:Streptococcus pneumoniae / Q000145:classification
keywords: Bayesian inference; Continuous-time Markov chains; Epidemiology; Multi-strain models; Transmission dynamics